Embedded System Development for Detection of Railway Track Surface Deformation Using Contour Feature Algorithm
作者机构:NCRA-HHCMS LabMehran University of Engineering&Technology Jamshoro76062Pakistan Department of Electronic EngineeringQuaid-e-Awam University of EngineeringScience&Technology Nawabshah67480Pakistan Design and Creative TechnologyTorrens University Australia196 Flinders StreetMelbourneVIC 3000Australia Department of Electronic EngineeringMehran University of Engineering and TechnologyJamshoro76062Pakistan Department of Electronics and Power EngineeringPakistan Navy Engineering CollegeNUST Karachi75350Pakistan
出 版 物:《Computers, Materials & Continua》 (计算机、材料和连续体(英文))
年 卷 期:2023年第75卷第5期
页 面:2461-2477页
核心收录:
学科分类:08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程]
基 金:supported by the NCRA project of the Higher Education Commission Pakistan
主 题:Railway track surface faults condition monitoring system fault detection contour detection deep learning image processing rail wheel impact
摘 要:Derailment of trains is not unusual all around the world,especially in developing countries,due to unidentified track or rolling stock faults that cause massive casualties each *** this purpose,a proper condition monitoring system is essential to avoid accidents and heavy ***,the detection and classification of railway track surface faults in real-time requires massive computational processing and memory resources and is prone to a noisy ***,in this paper,we present the development of a novel embedded system prototype for condition monitoring of railway *** proposed prototype system works in real-time by acquiring railway track surface images and performing two tasks a)detect deformation(i.e.,faults)like squats,shelling,and spalling using the contour feature algorithm and b)the vibration signature on that faulty spot by synchronizing acceleration and image data.A new illumination scheme is also proposed to avoid the sunlight reflection that badly affects the image acquisition *** contour detection algorithm is applied here to detect the uneven shapes and discontinuities in the geometrical structure of the railway track surface,which ultimately detects unhealthy *** works by converting Red,Green,and Blue(RGB)images into binary images,which distinguishes the unhealthy regions by making them white color while the healthy regions in black *** have used the multiprocessing technique to overcome the massive processing and memory *** embedded system is developed on Raspberry Pi by interfacing a vision camera,an accelerometer,a proximity sensor,and a Global Positioning System(GPS)sensors(i.e.,multi-sensors).The developed embedded system prototype is tested in real-time onsite by installing it on a Railway Inspection Trolley(RIT),which runs at an average speed of 15 km/*** functional verification of the proposed system is done successfully by detecting and recording the various railway track surface faul